
Motion Mechanisms.
Previous research has shown that a valuable feature for achieving high accuracy for video recognition is the relative motion across time (e.g, optical flow) [1]}. Furthermore, experiments that use motion information and visual features as input have been shown to outperform visual features alone [2]}, [3]}. Due to this, there has been much research on how to design motion mechanisms for video action recognition. TEA [4]} investigated different architectures and mechanisms which computed the difference between consecutive frames. TEIN [5]} investigated scaling the input by the motion information. TDN [6]} investigated multiple motion mechanisms and utilized pooling operations to leverage motion information at multiple spatial scales. However, these methods have not investigated how to utilize attention mechanisms for further performance improvements.
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